Detecting visual wake words (i.e. person detection) is a special case of image recognition. Table REF  lists some of the models proposed for performing wake words detection on the visual wake words dataset [1]}. Among all the proposed models, RaScaNet [2]} achieves the best balance of accuracy and resource usage. RaScaNet extracts features from an image patch using convolutional blocks, and then sequentially learns the latent representation of the entire image using recurrent blocks. The network also includes both spatial and channel attention to focus spatially distinct and multi-head discriminative feature representations.
